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Advances in Multimedia
Volume 2017 (2017), Article ID 7323681, 12 pages
https://doi.org/10.1155/2017/7323681
Research Article

SDP-Based Quality Adaptation and Performance Prediction in Adaptive Streaming of VBR Videos

1Hanoi University of Science and Technology, Hanoi, Vietnam
2The University of Aizu, Aizuwakamatsu, Japan

Correspondence should be addressed to Thoa Nguyen

Received 27 January 2017; Revised 8 May 2017; Accepted 24 May 2017; Published 2 July 2017

Academic Editor: Mei-Ling Shyu

Copyright © 2017 Thoa Nguyen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Recently, various adaptation methods have been proposed to cope with throughput fluctuations in HTTP adaptive streaming (HAS). However, these methods have mostly focused on constant bitrate (CBR) videos. Moreover, most of them are qualitative in the sense that performance metrics could only be obtained after a streaming session. In this paper, we propose a new adaptation method for streaming variable bitrate (VBR) videos using stochastic dynamic programming (SDP). With this approach, the system should have a probabilistic characterization along with the definition of a cost function that is minimized by a control strategy. Our solution is based on a new statistical model where the future streaming performance is directly related to the past bandwidth statistics. We develop mathematical models to predict and develop simulation models to measure the average performance of the adaptation policy. The experimental results show that the prediction models can provide accurate performance prediction which is useful in planning adaptation policy and that our proposed adaptation method outperforms the existing ones in terms of average quality and average quality switch.